Latest AI and machine learning research in psychiatry for healthcare professionals.
BACKGROUND: The field of psychiatry would benefit significantly from developing objective biomarkers that could facilitate the early identification of heterogeneous subtypes of illness. Critically, although machine learning pattern recognition methods have been applied recently to predict many psychiatric disorders, these techniques have not been utilized to predict subtypes of posttraumatic stres...
Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain-based disorders. However, some machine learning models have been criticized for requiring a large number of cases in each experimental group, and for resembling a "black box" that provides little or no insight into the nature of the data. In this art...
BACKGROUND: Low adherence to recommended treatments is a multifactorial problem for patients in rehabilitation after myocardial infarction (MI). In a ...
Cognitive behavioural therapy for psychosis (CBTp) involves helping patients to understand and reframe threatening appraisals of their psychotic exper...
Beck's insight-that beliefs about one's self, future, and environment shape behavior-transformed depression treatment. Yet environment beliefs remain ...
Recent studies have demonstrated that geographic location features collected using smartphones can be a powerful predictor for depression. While locat...
We investigated whether machine learning methods could potentially identify a subgroup of persons with autism spectrum disorder (ASD) who show vitamin...
Human-computer interaction (HCI) technology, and the automatic classification of a person's mental state, are of interest to multiple industries. In t...
Effective utilization of multi-center data for autism spectrum disorder (ASD) diagnosis recently has attracted increasing attention, since a large num...
OBJECTIVE: Structural MRI (sMRI) increasingly offers insight into abnormalities inherent to schizophrenia. Previous machine learning applications sugg...
Autism spectrum disorder is associated with significant healthcare costs, and early diagnosis can substantially reduce these. Unfortunately, waiting t...
Structural brain abnormalities in schizophrenia have been well characterized with the application of univariate methods to magnetic resonance imaging ...
Deep learning has become the new state-of-the-art for many problems in image analysis. However, large datasets are often required for such deep networ...
PURPOSE: In recent years, there has been an increase in the number of studies using social robots to improve psychological well-being. This systematic...
Multi-modal bio-sensing has recently been used as effective research tools in affective computing, autism, clinical disorders, and virtual reality amo...
Most psychiatric disorders emerge during childhood and adolescence. This is also a period that coincides with the brain undergoing substantial growth ...
BACKGROUND: A growing body of anecdotal evidence indicates that the use of robots may provide unique opportunities for assisting children with autism ...
Parkinson's disease (PD) is a neurodegenerative progressive disease that mainly affects the motor systems of patients. To slow this disease deteriorat...
Mental effort is an elementary notion in our folk psychology and a familiar fixture in everyday introspective experience. However, as an object of sci...
BACKGROUND: Technological advances are enabling us to collect multimodal datasets at an increasing depth and resolution while with decreasing labors. ...